The role of estrogen and progesterone, administered alone and in combination, in modulating cytokine concentration following traumatic brain injury
Bibliographic record
Abstract
Cytokines play an important role in the pathophysiology of traumatic brain injury (TBI). This study was designed to determine the effects of administering progesterone (P) and estrogen (E), alone and in combination, on brain water content, blood-brain barrier (BBB) disturbance, and brain level of cytokines following diffuse TBI. Ovariectomized rats were divided into 9 groups, treated with vehicle, E1, E2, P1, P2, E1+P1, E1+P2, E2+P1, and E2+P2. Levels of BBB disruption (5 h), cytokines, and water content (24 h) were evaluated after TBI induced by the Marmarou method. Physiological (E1 and P1) and pharmacological (E2 and P2) doses of estrogen and progesterone were administered 30 min after TBI. Water content in the E1+P2-treated group was higher than in the E1-treated group. The inhibitory effect of E2 on water content was reduced by adding progesterone. The inhibitory effect of E1 and E2 on Evans blue content was reduced by treatment with E1+P1 and E2+P2, respectively. The brain level of IL-1β was reduced in E1 and E2, after TBI. In the E2+P2-treated group, this level was higher than in the E2-treated group. The brain level of TGF-β was also elevated by the administration of progesterone and estrogen alone, and reduced when the hormones were administered in combination. In conclusion, a combined administration of progesterone and estrogen inhibited the decreasing effects of administration of progesterone and estrogen alone on water content and BBB disruption that mediated to change the proinflammatory cytokines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".